Student Scholar Symposium Abstracts and Posters
Document Type
Poster
Publication Date
Fall 12-4-2024
Faculty Advisor(s)
Franceli Cibrian
Abstract
This research aimed to assess the potential of Mazi Umntanakho ("Know Your Child") in tracking developmental milestones in young children. Mazi is a WhatsApp-based conversational agent that assists South African home visitors in evaluating and monitoring children's socio-emotional skills using the Strengths and Difficulties Questionnaire (SDQ) and the International Development and Early Learning Assessment (IDELA). A field study was conducted in low-income South African communities, where 95 home visitors assessed 1,208 children. This detailed analysis of the data was collected during that deployment, focusing on investigating whether assessment scores improved over time and whether the length of time between assessments impacted results.
To accomplish the aim, we followed a quantitative data analysis. Given that the data was collected in real conditions in a field study, we conducted an extensive data cleaning to filter out irrelevant or inconsistent entries, ensuring only significant and accurate data contributed to the results. From these refined datasets, we conducted an exploratory analysis and developed different data visualizations to illustrate score changes across various organizations and time intervals. From the datasets, we used a paired t-test, the analysis identified trends in score changes over a minimum number of days: >30, 60, 90, and 120 days. Results show no statistically significant difference overall, although scores tended to increase in follow-up assessments conducted after >90 days for SDQ and >60 days for IDELA.
Although there were non-statistical differences, our findings show that home visitors were able to use the Mazi app and collect relevant information from the children. More analysis will incorporate additional visualizations and cluster analyses to refine understanding of these assessments' effectiveness in varied settings.
Acknowledgments:
This study acknowledges the valuable contributions of Catherine E. Draper, Armando Beltran, and Gillian R. Hayes.
Recommended Citation
Murillo, Diego and Cibrian, Franceli L., "Statistical Analysis for Pre- and Post- Assessments of SDQ and IDELA Scores" (2024). Student Scholar Symposium Abstracts and Posters. 718.
https://digitalcommons.chapman.edu/cusrd_abstracts/718
Included in
Categorical Data Analysis Commons, Cognitive Science Commons, Data Science Commons, Development Studies Commons
Comments
Presented at the Fall 2024 Student Scholar Symposium at Chapman University.